The need for international transport of goods has increased for decennia, due to the globalisation of supply chains. The impact on the environment has gotten increased attention in the last few years, with different initiatives aiming to decrease transport emissions. While the EU has released a white paper stressing the need for modal shift towards rail transport, clear results remain absent. Therefore, this thesis explores improving the efficiency of the railway transport in the port area, specifically the port of Rotterdam. The goal is to both improve operational efficiency to decrease direct emissions, and incentivise modal shift towards railway transport.

To achieve these gains, an existing mathematical model is adapted to optimise the schedule for a single railway feeder services operator. A multi-objective function is minimised, to combine orders, reduce locomotive use and improve on time delivery. The model is benchmarked against a greedy algorithm, and structurally outperforms it.

Next, the feeder train services model (FTSM) is then used to investigate cooperative scheduling approaches. Cooperative game theory is used and the FTSM is run with stand-alone and pooled railway operators. For all scenarios tested, cooperating yields benefits, with cost reductions ranging from 25\% to 58\%, compared to stand-alone operation. The stable coalitions presented by this thesis present further gains in network capacity, as the pooled operators occupy less tracks.

This thesis fills the gap of port-specific railway freight transport, for which it both presents a novel mathematical model, and a cooperative strategy. The scenarios tested show benefits for all stakeholders, providing a solid base for further research and implementation.

Inland waterways offer a cost-effective, energy-efficient and relatively safer mode of freight transportation, and autonomous navigation presents an attractive opportunity to fully revitalise their utilisation. To enable a safe, efficient and reliable inland waterway ecosystem, autonomous inland vessels must account for uncertainties arising from environmental disturbances and modelling errors. In comparison to open-sea navigation, inland navigation involves confined waterways, frequent interaction with infrastructure and other vessels, and operational constraints that require both high situational awareness and constraint satisfaction. Furthermore, abnormal operating conditions resulting from critical sensor faults and failures must be tolerated through graceful performance degradation or, in the worst case, a fallback operation. In light of these operational requirements, this thesis investigates how autonomous vessels, especially (but not limited to) inland waterway vessels, can maintain safe, high-performance motion control while monitoring sensor faults and hazardous situations that affect their navigation.

More specifically, this thesis contributes an integrated framework that includes (a) a robust system identification methodology to obtain vessel maneuvering models for state estimation and prediction, (b) a Nonlinear Model Predictive Control (NMPC)-based control system that computes the vessel's control actions while satisfying the physical and operational constraints of inland waterways, (c) a multiple sensor Fault Detection and Isolation (FDI) scheme that monitors consistency in measurements by employing analytical redundancy relations and (d) a risk mitigation method that provides a fallback control action under complex failures.

Robust system identification for marine surface vessels

Maneuvering models play a central role in model-based control and monitoring system design by providing accurate estimates of the vessel's states and their future predictions. Identifying the parameters of a full-scale vessel from experimental data is particularly challenging due to significant modelling and measurement uncertainties. The first contribution of this thesis is a set-membership method for identifying key parameters of a nonlinear 3-Degrees of Freedom (3-DOF) vessel model that supports robust prediction and control design through a bounded error characterisation of the uncertainties. The identification process involves computing two sets: a Data-driven Parameter Set (DDPS) and a Feasible Parameter Set (FPS), using the system dynamics, uncertainty bounds and input-output measurements. Then, by solving quadratic programs over the FPS, parameter estimates and their uncertainty bounds are obtained. Validation results from full-scale trials demonstrate improved prediction accuracy and reduced computational time. In addition, through sensitivity analysis, the parameters most crucial for identification performance are identified.

Path-following control of inland waterway vessels in confined waterways

Inland waterways are characterised by tight operational and environmental constraints, leading to explicit control design specifications. The model predictive control methodology is adopted, as it naturally integrates multi-variable dynamics, actuators, state, environmental constraints and objectives to optimise performance and control effort. An NMPC path-following control scheme is proposed for Inland Waterway Vessels (IWVs), with the prediction model tailored to the hydrodynamic phenomena in confined waterways, including bank and shallow-water effects. Many challenging scenarios are considered for validating the control scheme through simulations, such as turning at a steep river confluence, sailing a curved river and avoiding a static obstacle. The impact of reduced ship-bank distances, propulsion speeds and river cross-section shapes further provides insights into control performance and design choices. In addition, key performance metrics are proposed to evaluate the controller's performance and quantify path-following accuracy, robustness and safety.

Multiple sensor fault diagnosis of autonomous surface vessels

Autonomous vessels rely on multiple heterogeneous sensors for navigation, motion control and situational awareness. Sensor faults may propagate through measurements to interconnected systems on board, thereby impacting downstream decisions. This thesis proposes a multiple-sensor FDI scheme that exploits Analytical Redundancy Relations (ARRs) derived from the vessel's dynamical model and adaptive thresholds to diagnose sensor faults.

The design methodology adopted in the proposed scheme includes (a) the generation of fault detection residuals having structural sensitivity to one or more sensor faults and (b) the computation of adaptive thresholds used for residual bounding with robustness against environmental and modelling uncertainties. As a result, false alarms can be avoided in the fault detection process. In addition, a combinatorial fault decision logic is designed, enabling the scheme to not only detect fault occurrence but also to determine the compromised sensors. Combined, the structurally sensitive residuals and the decision logic facilitate the isolation of multiple sensor faults. The proposed fault diagnosis scheme is suitable for continuous monitoring of faults during vessel operation, while easily accommodating variations in the vessel's actuator or sensor configurations. Furthermore, by identifying weak fault sensitivity by evaluating residuals with respect to fault magnitudes, improved fault isolation decisions are obtained.

Collision and grounding risk mitigation of inland waterway vessels

Finally, the risk mitigation of autonomous vessels is explored by considering the underlying sub-problems of risk modelling and control. For risk modelling, a Bayesian Belief Network (BBN) is built from hazard analysis results, providing transition probabilities for sequential decision-making. Thereafter, a Partially Observable Markov Decision Process (POMDP) model is designed to represent the vessel's states and provide a suitable higher-level control strategy that ensures the vessel's safety by preventing hazardous situations, such as grounding and collisions. The method is verified through an inland waterway navigation case study, which demonstrates SCS selection reliably during a complex failure scenario.

Autonomous surface vessels are increasingly expected to operate cooperatively in future waterborne transport systems, where multiple vessels can sail in coordinated formations to improve safety, efficiency, and operational capability. However, when vessels operate in close proximity, ship-to-ship hydrodynamic interactions become non-negligible. These interactions influence maneuvering dynamics, tracking performance, formation stability, and propulsion energy demand. Conventional formation control methods often treat such interactions as external disturbances or ignore them entirely, which limits their applicability to energy-efficient and interaction-sensitive multi-vessel operations. This thesis addresses this gap by developing hydrodynamics-aware model predictive formation control methods for energy-efficient multi-vessel systems.

The thesis first reviews cooperative formation control strategies, communication structures, hydrodynamic interaction mechanisms, and formation-resistance characteristics for autonomous surface vessels. Based on this synthesis, a conceptual framework is proposed in which ship-to-ship interactions are not only treated as disturbances to be compensated, but also as predictable physical couplings that can be exploited for energy-aware formation design. An interaction-aware model predictive control framework is then developed for multi-vessel formation tracking. A three-degree-of-freedom vessel model is combined with a data-informed ship-to-ship interaction model, allowing surge, sway, and yaw interaction effects to be incorporated into the prediction and control process. Simulation studies demonstrate that explicitly considering interaction forces improves tracking robustness and provides a more realistic basis for formation control in close-spacing regimes.

Building on this interaction-aware control foundation, the thesis further investigates hydrodynamics-aware formation optimization for reducing fleet-level energy consumption. A hierarchical control architecture is designed, where an upper-level decision layer optimizes formation configuration and reference speed based on interaction-aware energy indicators, while a lower-level model predictive controller tracks the resulting references under vessel dynamics and actuator constraints. Different formation layouts, including tandem, triangular, echelon, and adaptive configurations, are examined to reveal the trade-offs between energy saving, formation tracking accuracy, and stability. The results show that energy-efficient formations are strongly speed- and geometry-dependent, and that favorable hydrodynamic interaction regions can be used to reduce resistance and propulsion demand.

Finally, the thesis extends the framework to route-following operations under environmental disturbances. A leader–follower MPC structure with disturbance estimation is proposed to improve post-turn spacing recovery and maintain interaction-favorable geometries under wind, current, and sea-state-related energy effects. Compared with centralized MPC, the leader–follower formulation keeps the fleet more persistently in energy-saving regimes. In the studied route scenario, the LF-MPC architecture achieves a mission-average energy-consumption index of −4.17%, whereas the centralized MPC case results in a positive average index of +1.28%. These findings indicate that hydrodynamics-aware formation control can provide additional energy-saving potential beyond conventional single-vessel optimization, while preserving formation tracking performance and operational feasibility.

Overall, this thesis contributes a systematic framework for integrating ship-to-ship hydrodynamic interactions into model predictive formation control. It demonstrates that interaction-aware prediction, configuration optimization, and hierarchical control can jointly support energy-efficient, robust, and adaptable multi-vessel operations. The results provide a foundation for future research on scalable distributed control, propulsion-inclusive energy optimization, and real-world deployment of cooperative autonomous vessel formations.

This thesis explores the integration of Maritime Autonomous Surface Ships (MASS) into Mixed Waterborne Transport Systems (MWTS), addressing critical challenges in ensuring navigational safety and operational efficiency. Recognising the complexities of interactions in MWTS, especially in scenarios without direct communication between vessels, the research develops a decision-making framework that integrates situational awareness, human-preference-aware navigation, and trust dynamics. These components collectively aim to support seamless interactions between autonomous and manned vessels, ensuring safe and efficient navigation in the MWTS. The proposed framework builds on a systematic exploration of key challenges in MASS operations. For situational awareness, an ontology-driven knowledge maps model is introduced, enabling MASS to integrate multi-source data and maritime regulations. This model is further combined with a Dynamic Window Approach (DWA) path planner, allowing for real-time compliance with COLREGs and proactive collision avoidance. The research also advances human-preference-aware navigation by extracting and modelling navigational behaviours of manned vessels using AIS data. An LSTM-autoencoder with clustering methods is utilised to identify navigational preferences, which are then incorporated into a trajectory prediction model based on Multi-Task Learning Sequence-to-Sequence LSTM with attention (MTL-Seq2Seq-LSTM-Att) architectures. This integration enhances MASS decision-making by aligning manoeuvring strategies with human operators’ expectations, reducing the likelihood of misinterpretation in mixed traffic scenarios.

Increasing congestion and environmental pressures in urban logistics, alongside growing population demands, necessitate innovative solutions. This thesis tackles synchronized two-echelon routing problems within multimodal logistics, specifically exploring the potential of integrating waterborne transport. By developing optimization models and solution approaches for the comprehensive design and management of these systems—encompassing strategic location, tactical allocation, and operational routing and synchronization—the results promote a transition toward more sustainable and efficient urban logistics, ultimately fostering healthier, more livable, and economically vibrant cities.

This thesis focuses on enabling safe and reliable navigation for Autonomous Surface Vessels (ASVs) operating in complex and mixed-traffic maritime environments. It presents a suite of motion planning and control algorithms that ensure fault tolerance and compliance with maritime traffic rules, even under uncertainty and component failures. Key contributions include a Model Predictive Contouring Control (MPCC) method that formalizes COLREGs compliance, a model-based fault diagnosis framework using residual analysis, a robust Set-Membership Estimation (SME) approach for fault parameter identification, and a Robust Adaptive Model Predictive Control (RAMPC) scheme that integrates fault information into trajectory optimization. Validated through extensive simulations and implemented in ROS, the proposed framework demonstrates robust performance in dynamic and uncertain conditions, laying the groundwork for real-world deployment of autonomous maritime systems.

This paper investigates how two sequential sub-processes at a flower auction can be well aligned to efficiently execute the overall auction process. Existing literature mainly focuses on warehouses without perishable goods and warehouses where all orders are known before the outbound process is started. However, at a flower auction, the gathering of goods and distribution takes place while the auction is still ongoing. In addition, flowers are vulnerable goods that must be handled with care.
During a case study at Royal FloraHolland Naaldwijk, the current process of order picking and in-house delivery is investigated to find the main strengths and bottlenecks. This is done physically and with data. From this analysis,
it has been found that the main issues are the spread and share of waiting times in the in-house delivery process and the output of the order-picking process that is too low. To improve the overall process based on the found issues, a calculation model has been built in Python to test possible improvements. It has been found that implementing limited waiting times and other alterations to increase efficiency results in a more reliable and better predictable
process that can be executed with approximately the same number of work hours or slightly more than in the current situation.

Container transport requires constant improvement to be more efficient and sustainable. Improvements can take the form of innovations, investments or policies. This thesis develops choice-driven methods to enhance decision-making in intermodal freight transport by focusing on demand, supply, and competition aspects considering heterogeneous behaviors and interactions of actors. The proposed methods support policy-makers, transport companies, and planners in foreseeing the impacts of their decisions on the involved actors.

Maritime shipping is essential for global trade, requiring the coordinated efforts of shipping lines, ports, and logistics providers. Early collaborative efforts focused on enhancing competitiveness, but the emphasis has shifted towards achieving environmental sustainability and resilience. This thesis provides collaborative approaches for resilient and decarbonized maritime and port operations, highlighting the importance of stable cost allocation methods to foster strong and lasting collaboration incentives.

As the energy transition progresses and vessel autonomy increases, the control of marine systems is gaining greater significance. This thesis develops safe and resilient control methods for marine Power and Propulsion Plants (PPPs). The proposed methods include fault diagnosis techniques and frameworks for managing the effects of malfunctions and system changes after mission updates, aiming to improve the safety and adaptability of marine PPPs in the evolving maritime industry.

This research paper focuses on improving the performance of cross-docking operations under uncertainty in the context of e-commerce logistics. The growth of e-commerce sales has increased product returns and complexity to supply chains. To address this issue, this study investigates how cross-docking operations can be improved under external and internal uncertainty factors. The research begins with a literature review to understand cross-docking facilities (CDFs) and measures to mitigate the effects of uncertainty. The current state of a CDF in a case study for a Fourth Party Logistics (4PL) provider is examined, and by reflecting on the literature overview, two potential means for decreasing the effects of uncertainty are identified: staging-level design and load carrier-type design.

A Discrete Event Simulation (DES) model is developed to test the effects of staging-level design and load carrier types on the performance of the CDF. The simulation model captures input factors such as truck arrivals, freight levels, and the purity level of cross-docking. The simulation model’s performance is tested for different scenarios, and the effects of different design alternatives are analyzed.

The results demonstrate that two-stage cross-docking with pallets can significantly reduce the total makespan and improve operational efficiency compared to single-stage cross-docking with pallets. The results also show that using roll containers significantly decreases the chance of intra-terminal congestion but also results in longer unloading and reloading times. The research contributes to the understanding of cross-docking operations under uncertainty, stresses the importance of staginglevel
and load carrier type design on CDF performance, and provides insights for logistics companies seeking to optimize their e-commerce supply chains.

Efficient maintenance scheduling is critical to ensure the reliability and cost-effectiveness of production lines. Traditional approaches often rely on corrective maintenance and limited preventive maintenance, which can lead to unplanned downtime and higher operational costs. This research investigates the impact of predictive maintenance, supported by a condition-based model, on the reliability and cost performance of a production line. Due to insufficient real-world condition monitoring data, a synthetic dataset was generated based on historical process data and literature assumptions. This dataset simulates the running time, probability of cavitation, and operational load conditions for valves and pumps across the production line, allowing the model to provide failure probability estimates and recommend maintenance interventions before production begins.

The study compares two scenarios: the first follows conventional maintenance practices with primarily corrective actions and minimal preventive maintenance during scheduled production-free weeks; the second scenario integrates the predictive maintenance model to guide both preventive and predictive interventions. Key performance indicators (KPIs) are defined to evaluate the effect of the model on line reliability and maintenance costs. The primary KPI, the Maintenance Downtime Index (MDI), measures the ratio of planned maintenance hours to total downtime hours, reflecting the proportion of downtime that is scheduled versus unplanned. Additional KPIs analyze the distribution of maintenance costs among corrective, preventive, and predictive actions, with higher proportions of predictive maintenance indicating improved reliability.

Results demonstrate significant benefits of using the predictive maintenance model. The MDI shows a reduction of 5% in total downtime hours due to fewer unplanned interruptions and a greater allocation of downtime to planned maintenance activities. Maintenance cost analysis reveals a 53% reduction in total costs when predictive maintenance is applied. Furthermore, the proportion of corrective maintenance costs decreases substantially, confirming that the model effectively shifts maintenance efforts from reactive to proactive interventions. These findings indicate that predictive maintenance enhances both operational reliability and cost efficiency, supporting more informed decision-making by operators and maintenance planners.

The study highlights the importance of integrating condition-based predictive models into production scheduling, even when limited real-time data is available. Synthetic datasets, grounded in historical data and validated assumptions, provide a viable approach to evaluating predictive maintenance strategies and their impact on key operational metrics. By prioritizing predictive interventions over corrective actions, production lines can achieve lower downtime, improved reliability, and reduced maintenance expenditure. The findings offer practical guidance for manufacturing operators seeking to optimize maintenance strategies and support the broader adoption of predictive maintenance in industrial settings.

A relatively new concept within demand management for time window assignment is green labeling; time windows which contribute to improving the routing performance in terms of sustainability. Certain time slots are given a so-called green label. From literature, limited information is known about the choice preference with regard to green labeling and its impact on routing performance. Via choice modeling, certain attributes are estimated based on a data set from an e-grocer. Together with a beta estimate on green labeling from literature, the effect of green labeling on choice behavior is analyzed. The results are used for route optimization where the effect of various static and dynamic approaches are tested. Results show that dynamic green labeling has the most promising effect on routing performance in terms of costs and sustainability. Especially when customers are more nudged toward the largest time windows in less popular day parts. The CO2 emissions decrease by 127.4 CO2 per order and the costs decrease by 1.03 euro per order on average. These results are based on the specific situation of the e-grocer in the case study. With regard to choice modeling, this research is limited to only time window characteristics. The improvements in terms of costs and sustainability per green labeling approach, based on the attributes resulting from real-data choice modeling, contribute to more knowledge on the effect of green labeling on routing performance.

The demand for accurate and effective cool chains has been expected to increase, especially for pharmaceuticals in the air freight industry. However, several problems and challenges remain such as cool chain breaks and cool storage capacity constraints while there are rarely routine systems in place for consistent insight into the operational quality of such systems. Besides, the concept of a DT has received increasing attention in the literature, while a multitude of applications have been found in fresh cool chains. Therefore, the DT concept has been applied to a pharmaceutical cool chain for operational quality improvement. Firstly, a novel operational quality metric has been proposed: the OCCE. Consequently, a cool chain at KLM Cargo has been studied and modelled by means of the DES technique in order to derive a virtual representation. Consequently, the DT concept has been applied through the implementation of a decision support module for cool storage decision-making. The model implementation has shown that the cool chain operational quality has been improved while the average exposure of freight has decreased.

Offshore wind energy can play a key role in the energy transition. Reducing installation costs for offshore wind installation projects helps to be cost-competitive with other renewable energy technologies. Installation costs can increase when the installation project planning is delayed. Literature shows that offshore wind installation projects are delayed by weather conditions exceeding operational limits and downtime caused by vessels and equipment. However, the magnitude and causes of downtime due to equipment breakdown are unclear. Additionally, no method is found in the literature to reduce downtime due to equipment breakdown in offshore wind installation. Data-analysis of observed failure data shows that equipment breakdown causes downtime during pinpile installation during the spring and summer seasons. Root-cause analysis indicates that scheduled preventive maintenance is often postponed on the critical path when weather conditions are favourable for installation. These decisions are made based on knowledge of oil and gas projects, but that knowledge is not applicable anymore. In this study a new method is proposed, which includes equipment characteristics and preventive maintenance on the critical path of an installation schedule, using discrete-event simulation (DES). In the DES model, four designs are simulated to gain insight into the effect of decision-making on the critical path on key performance indicators. The designs are based on the planned maintenance pillar of the Total Productive Maintenance framework. Implementing equipment breakdown and preventive maintenance in the installation schedule gives insight into the effects of decision-making before project execution. The results of this study indicate that the downtime due to breakdown and preventive maintenance of the hammer can be reduced by 10% if preventive maintenance is prioritized on the critical path.

The present study was designed to gain a deeper understanding of RTI control and Supply Chain Coordination, with a specific focus on the practical challenges experienced in the reverse logistics by asset-owner and asset-users. A case study at EPS - an RTI owner - is performed and a practical problem has been addressed with a design science and TIL systems engineering approach. It was observed that a huge amount of deposit is allocated to a retailer's generic account. The current state analysis found that this problem is mainly due to inefficiency in the current identification process with physical SSCC labels. The design goal of minimising the "Delta" -the number of unidentifiable return packages- was defined. Ways to redesign the reverse logistics were determined with the aim that all return packages can be allocated to the retailer at shop level. Based on the future state requirements and the design goal, the concept of matchmaking is developed as a way to achieve the design goal. This thesis shows that a matchmaking system can improve the traceability of return packages and that it has the potential in bringing the Delta of return packages with missing shoplink to zero.
The matchmaking system is defined as the framework that ensures matchmaking, which uses a "key" generated by the sender to represent the return package. If the receiver can find the key upon arrival of the return package at the depot, the sender can be identified. Seven matchmaking systems were considered. Five alternatives use unique tray identities as key. One alternative uses the load carrier as key and the last alternative uses the return package identity as key. The validation results show that an "all read" or "reading of all individual trays" is not a requisite for a working matchmaking system. By contrast, as long as a certain ratio of reading at two locations is reached, an all-read scenario can be mimicked. The assessment investigated the instances in which zero mismatch take place. Results show that the higher the data capture capability of both the sender and receiver, the higher the chance a match can take place and the smaller the chance of a mismatch. This thesis creates insights on requirements for enhancing traceability of RTI's in the return chain and developed a matchmaking concept that can address the practical problem of returns without traceability of shop origin. The developed matchmaking concept is the outcome of an analysis of the current state and makes use of data elements that are already being collected in the database, in the case of EPS. The study addresses how collected data can be leveraged for enhanced RTI management in the reverse logistics and may inspire practitioners to face challenges with a similar lean approach.

Offshore lifting operations must have reduced payload motion to increase safety and reduce operating time. When payload is retrieved from the splash zone to the deck, besides the crane block, no additional control can be applied on the underactuated system. Existing studies either assume more control over the payload or develop a control system based on a new crane. To reduce payload motion on current crane vessels, a conceptual model needs to be developed.

In this thesis, various state-of-the-art solutions are considered based on four criteria: Time reduction, motion reduction, initial investment required and power required. Eventually, the quantified criteria and an analytic hierarchy process established that the most promising concept is based on an automated side loader of a garbage truck.

The selected concept is developed based on a design process that focuses on optimized material usage. The geometry is determined according to requirements and forms the starting point of the circular design process. A dynamic analysis is conducted to obtain the dynamic response of the payload and eventually the reduced payload motion. The design cycle is complete after a finite element analysis has been conducted to verify the structural integrity of the model. After more than 20 cycles of the design process, the conceptual model is optimized and over 85% of motion is reduced in the X direction. The payload motion in Y- and Z-direction is 20% and 29% respectively.

The simulation results in this study show that the conceptual model is able to reduce the payload motion during offshore lifting operations whilst staying within the limits set by offshore standards. The motion reduction of the payload creates a safer and more efficient environment to execute offshore lifting operations.

Abstract
Purpose - The aim of this paper is to propose a method for performance measurement of the livestock feed industry from an environmental and economic perspective. There is a knowledge gap both in the literature and society in the field of performance measurement of the livestock feed industry with environmental concerns. Livestock feed companies need support in the decision-making process to produce environmentally sustainable livestock feed at as low as possible costs.
Design/methodology/approach – An environmental performance index for livestock feed is constructed based on techniques of the min-max transformation, the analytic hierarchy process and simple additive weighting. The verified environmental performance index for livestock feed is brought into practice; different scenarios, which all represent a feed composition, are quantitatively compared to a benchmark feed composition. The environmental performance index is validated with an uncertainty analysis and sensitivity analysis.
Findings – The constructed environmental performance index for livestock feed is assessed by comparing nine scenarios against a benchmark feed composition. The results indicate that the environmental performance index for the livestock feed industry is technically feasible and effective to measure the performance of the livestock feed industry from an environmental and economic perspective.
Research implications – The constructed environmental performance index for
livestock feed can serve as a decision-making tool for livestock feed companies. As a response to climate regulations and a market pull, this paper provides a tool for livestock feed companies to select livestock feed compositions with the best performance based on environmental sustainability and economic performance.
Originality/value – A new performance measurement method is designed for an
environmental performance index for the livestock feed industry. From the literature, environmental sustainability variables are identified. The data on the environmental performance are obtained from a public database as well as a livestock feed company. The created index can contribute to decision-making in the livestock feed industry.

This thesis has provided insight into how machine learning can be beneficial to path planning in container terminals. Path planning algorithms can be used in environments with automated vehicles. A well known algorithm is the A* path planning algorithm, which is the fastest optimal path planning algorithm under satisfied conditions. However, the behaviour of a container terminal is unknown beforehand, costs can change over iterations. Therefore, Liu et al. [Liu et al., 2019] and Keselman et al. [Keselman et al., 2018] show the advantage of combining A* with Machine Learning. This way, the exploring part of the ML algorithm is combined with the fast andmore precise properties of the A* PP algorithm. This thesis has proposed the machine learning algorithm Vehicle Aware Reinforcement Learning Path Planning Algorithm VARLPPA. This algorithm uses Monte Carlo Control method. This is a model free approach, which has been shown in both experiments to find more efficient solutions in exceptional situations.

There has been a growing interest in improving the operating efficiency of warehouses, which is one of the critical facilities used in the logistics sector. This thesis examines the impact of order characteristics on the performance of different configurations of the outbound logistics of a 3PL warehouse. A contingency approach combined with a proof of configuration method is used to answer this question. The contingency variables in this study are the order characteristics, which are uncertain for the future state of the newly built e-commerce warehouse Haaften III for Nedcargo Logistics, A third-party logistics provider in the Netherlands. Six contingency scenarios are chosen, and a model transforms them into experiments. Three different potential warehouse configuration models process these experiments, and their productivity performance is compared and analysed. Our results showed that the compactness of the layout, ABC-class-based storage and a new picking strategy, Star Aisle Batch combined with Singlepicks, provides the best productivity in each scenario. Next, between each scenario, the productivity is impacted differently. The productivity is higher if the order characteristics contain a low percentage of A-type products, the amount of colli is high, or orderlines per order are low. A change in configuration or strategy within each scenario also influences productivity differently. Lastly, it is also proved that the new proposed configuration and picking strategy can improve its current productivity by over 30%.